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AI-Powered Data Catalogs: Enhancing Data Discovery and Understanding

EasyChair Preprint no. 13211

17 pagesDate: May 7, 2024


AI-Powered Data Catalogs have emerged as an essential tool in the era of big data and complex data landscapes. This abstract explores the concept of AI-Powered Data Catalogs and their role in enhancing data discovery and understanding.


The abstract begins by highlighting the importance of data discovery and understanding in today's data-driven organizations. It emphasizes how efficient data management and analysis rely on locating and comprehending relevant data assets.


The abstract then introduces AI-Powered Data Catalogs as a solution to address the challenges associated with data discovery. These catalogs leverage artificial intelligence and machine learning techniques to automate various aspects of data management, including data ingestion, metadata management, search and discovery, as well as data lineage and relationships.


The abstract outlines the key components of AI-Powered Data Catalogs, including data ingestion and integration, metadata management, search and discovery, and data lineage and relationships. It explains how these components work together to provide a comprehensive and intelligent cataloging system.

Keyphrases: AI-Powered Data Catalogs, data discovery, Data Governance, data management

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Harold Jonathan and Edwin Frank},
  title = {AI-Powered Data Catalogs: Enhancing Data Discovery and Understanding},
  howpublished = {EasyChair Preprint no. 13211},

  year = {EasyChair, 2024}}
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